We review theory and methodology of the class of simultaneous graphical dynamic linear models (SGDLMs) that provide flexibility, parsimony and scalability of multivariate time series analysis. Discussion includes core theoretical aspects and summaries of existing Bayesian methodology for forward filtering and forecasting with SGDLMs. The review is complemented by new theory linking dynamic graphical and factor models, and extensions of the Bayesian methodology. This addresses graphical structure uncertainty via model marginal likelihood evaluation, and analysis with missing data relevant to counterfactual analysis. The latter advances the ability to scale causal analysis to higher-dimensional time series. Aspects of the theory and methodology are exemplified in a global macroeconomic time series study with time-varying cross-series relationships and primary interests in potential causal effects. The example highlights the utility of SGDLMs with insights generated by the theoretical structure of these models, and benefits of fully Bayesian assessment of post-intervention outcomes in causal time series studies as in prediction more generally.

翻译：本文综述了同步图动态线性模型（SGDLMs）的理论与方法论，该类模型为多元时间序列分析提供了灵活性、简约性和可扩展性。讨论涵盖了SGDLMs前向滤波与预测的核心理论要点及现有贝叶斯方法论的总结。本综述通过连接动态图模型与因子模型的新理论，以及对贝叶斯方法论的扩展得以补充。这通过模型边际似然评估处理图结构不确定性，并针对与反事实分析相关的缺失数据进行分析。后者提升了将因果分析扩展至高维时间序列的能力。理论与方法论的多个方面在一个全球宏观经济时间序列研究中得到例证，该研究涉及时变的跨序列关系，并主要关注潜在因果效应。该案例凸显了SGDLMs的实用性——这些模型的理论结构能产生深刻见解，并展示了在因果时间序列研究中（与更广泛的预测研究类似）对干预后结果进行完全贝叶斯评估的优势。